Good solutions for Windows are, The Enthought Python Distribution (EPD) (which provides binary
installers for Windows, OS X and Redhat) and Python (x, y). Both of these packages include Python, NumPy and
many additional packages.

A lightweight alternative is to download the Python
installer from www.python.org and the NumPy
installer for your Python version from the Sourceforge download site

The NumPy installer includes binaries for different CPU’s (without SSE
instructions, with SSE2 or with SSE3) and installs the correct one
automatically. If needed, this can be bypassed from the command line with

Most of the major distributions provide packages for NumPy, but these can lag
behind the most recent NumPy release. Pre-built binary packages for Ubuntu are
available on the scipy ppa. Redhat binaries are
available in the EPD.

Make sure that the Python package distutils is installed before
continuing. For example, in Debian GNU/Linux, distutils is included
in the python-dev package.

Python must also be compiled with the zlib module enabled.

Compilers

To build any extension modules for Python, you’ll need a C compiler.
Various NumPy modules use FORTRAN 77 libraries, so you’ll also need a
FORTRAN 77 compiler installed.

Note that NumPy is developed mainly using GNU compilers. Compilers from
other vendors such as Intel, Absoft, Sun, NAG, Compaq, Vast, Porland,
Lahey, HP, IBM, Microsoft are only supported in the form of community
feedback, and may not work out of the box. GCC 3.x (and later) compilers
are recommended.

Linear Algebra libraries

NumPy does not require any external linear algebra libraries to be
installed. However, if these are available, NumPy’s setup script can detect
them and use them for building. A number of different LAPACK library setups
can be used, including optimized LAPACK libraries such as ATLAS, MKL or the
Accelerate/vecLib framework on OS X.

The two most popular open source fortran compilers are g77 and gfortran.
Unfortunately, they are not ABI compatible, which means that concretely you
should avoid mixing libraries built with one with another. In particular, if
your blas/lapack/atlas is built with g77, you must use g77 when building
numpy and scipy; on the contrary, if your atlas is built with gfortran, you
must build numpy/scipy with gfortran. This applies for most other cases
where different FORTRAN compilers might have been used.

One relatively simple and reliable way to check for the compiler used to build
a library is to use ldd on the library. If libg2c.so is a dependency, this
means that g77 has been used. If libgfortran.so is a a dependency, gfortran
has been used. If both are dependencies, this means both have been used, which
is almost always a very bad idea.

You can install the necessary packages for optimized ATLAS with this command:

sudo apt-get install libatlas-base-dev

If you have a recent CPU with SIMD suppport (SSE, SSE2, etc...), you should
also install the corresponding package for optimal performances. For example,
for SSE2:

sudo apt-get install libatlas3gf-sse2

This package is not available on amd64 platforms.

NOTE: Ubuntu changed its default fortran compiler from g77 in Hardy to
gfortran in Intrepid. If you are building ATLAS from source and are upgrading
from Hardy to Intrepid or later versions, you should rebuild everything from
scratch, including lapack.